Assessing the performance of a distributed radiation-temperature melt model on an Arctic glacier using UAV data
Bibliographic record
Abstract
Abstract. Enhanced temperature index (ETI) models of glacier surface melt are commonly used in studies of glacier mass balance and runoff. With limited data available most models are validated based on ablation stakes and data from automatic weather stations (AWS). With the technological advances of unmanned aerial vehicles (UAVs) and structure-from-motion (SfM), it is possible to measure glacier surface melt in detail over larger portions of a glacier. In this study, we use melt measured using SfM processing of UAV imagery to assess the performance of an ETI melt model in two-dimensions. Imagery collected over a portion of the ablation zone of Fountain Glacier, NU, on July 21 and 24, 2016 was previously used to determine distributed surface melt. Incoming solar radiation and temperature measured at the AWS, along with albedo derived from UAV imagery, are used as inputs for the model which was used to estimate melt from July 21–24, 2016. Modelled melt agrees with melt measured at the AWS within ±0.010 m. Across the study area the median model error (−0.044 m), calculated as the difference between measured and modelled melt, is within the uncertainty of the measurements. A strong link was found between the model error and glacier surface aspect with higher errors linked to south aspects. The highest errors were also linked to the density of water flow features on the glacier surface. The relation between water flow and model error suggests that energy from surface water flow is contributing significantly to surface melt on Fountain Glacier. Deep surface streams with highly asymmetrical banks are observed on Fountain Glacier, but the processes leading to their formation are missing in the model assessed here. The failure of the model to capture flow-induced melt and to under-estimate melt on south aspects would lead to significant underestimation of surface melt should the model be used to project future change.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".